Diabetes, obesity, metabolic syndrome, and cardiovascular disease are major causes of morbidity and mortality in the USA and worldwide. In the United States, ~34% of adults age e 20 years harbor a set of metabolic risk factors that include abdominal obesity, insulin resistance, atherogenic dyslipidemia, a proinflammatory state, and elevated blood pressure. Substantial evidence exists supporting a genetic component in the etiology of these traits. Our overall goal is to identify genetic variants that are responsible for variability in metabolic traits and risk to the related diseases. In this proposal, we aim to detect both rare variants and regulatory variants to further understand the genes, mechanisms, and pathways that influence obesity, metabolic syndrome, and diabetes. Large population cohorts are needed to identify less common (.005 d MAF < .05) and rare (MAF < .005) genetic determinants, to dissect the genetic contributions to correlated traits, and to evaluate the relative effects of and interactions between genes, environment, and behavior. One of the largest single-site population-based cohorts in which to evaluate genetic determinants of metabolic traits, the METSIM cohort of 10,197 individuals was ascertained in Kuopio, Finland during 2005- 2010. Participants were subjected to extensive clinical exams including oral glucose tolerance tests, body composition analysis, and measurement of plasma biomarkers and metabolites, and behavioral and clinical diagnostic data were collected for diabetes, diabetes complications, and cardiovascular events. In the context of the METSIM study, we will sequence total genomic DNA from 1,000 individuals at >4X coverage and impute genetic variants into 9,197 additional METSIM individuals. We will test variants for association with up to 200 metabolic quantitative traits and follow up association results via imputing variants into >15,000 additional samples. Using subcutaneous adipose samples from a subset of 400 METSIM participants, we will identify allele-specific differences in adipocyte expression and test potentially causal metabolic disease variants for functional regulatory effects. In addition, we will assess evidence for interactions and causal relationships between metabolic traits. Through this work we expect to identify novel genetic determinants of metabolic traits, discover pathogenic regulatory variants, and determine multivariate genetic, regulatory, and environmental relationships that lead to diabetes, obesity, and the metabolic syndrome. Better understanding of these factors and mechanisms may lead to clearer characteristics of disease subgroups and more targeted diagnoses and treatments.